Analysis of Linear and Nonlinear dimensionality Reduction Methods for Gender Classification of Face Images

Abstract

Data in many real world applications are high dimensional and learning algorithms like neural networks may have problems in handling high dimensional data. However, the Intrinsic Dimension is often much less than the original dimension of the data. Here, we use fractal based methods to estimate the Intrinsic Dimension and show that a nonlinear projection method called Curvilinear Component Analysis can effectively reduce the original dimension to the Intrinsic Dimension. We apply this approach for dimensionality reduction of the face images data and use neural network classifiers for Gender Classification.